干旱区生态环境知识资源中心(Arid): Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion

Arid

DOI	10.3390/agronomy12112658
	Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion
	Mu, Fei; Chu, Hongli; Shi, Shuaiqi; Yuan, Minxin; Liu, Qi; Yang, Fuzeng
通讯作者	Yang, FZ
来源期刊	AGRONOMY-BASEL
EISSN	2073-4395
出版年	2022
卷号	12 期号:11
英文摘要	This study uses UAV multi-spectral remote sensing images to carry out ground object classification research in complex wheat field scenes with diverse varieties. Compared with satellite remote sensing, the high spatial resolution remote sensing images obtained by UAVs at low altitudes are rich in detailed information. In addition, different varieties of wheat have different traits, which makes it easy to misclassify categories in the process of semantic segmentation, which reduces the classification accuracy and affects the classification effect of ground object. In order to effectively improve the classification accuracy of ground object in complex wheat field scenes, two Multi-Scale U-Nets based on multi-scale feature fusion are proposed. Multi-Scale U-Netl is a network model that adds a multi-scale feature fusion block in the copy process between U-Net encoding and decoding. Multi-Scale U-Net2 is a network model that adds a multi-scale feature fusion block before U-Net inputs an image. Firstly, the wheat field planting area of Institute of Water-saving Agriculture in Arid Areas of China (IWSA), Northwest A&F University was selected as the research area. The research area was planted with a variety of wheat with various types of traits, and some traits were quite different from one another. Then, multi-spectral remote sensing images of different high spatial resolutions in the study area were obtained by UAV and transformed into a data set for training, validation, and testing of network models. The research results showed that the overall accuracy (OA) of the two Multi-Scale U-Nets reached 94.97% and 95.26%, respectively. Compared with U-Net, they can complete the classification of ground object in complex wheat field scenes with higher accuracy. In addition, it was also found that within the effective range, with the reduction of the spatial resolution of remote sensing images, the classification of ground object is better.
英文关键词	multi-scale feature fusion U-Net UAV multi-spectral image complex wheat fields ground object classification
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000880718700001
WOS类目	Agronomy ; Plant Sciences
WOS研究方向	Agriculture ; Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391782
推荐引用方式 GB/T 7714	Mu, Fei,Chu, Hongli,Shi, Shuaiqi,et al. Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion[J],2022,12(11).
APA	Mu, Fei,Chu, Hongli,Shi, Shuaiqi,Yuan, Minxin,Liu, Qi,&Yang, Fuzeng.(2022).Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion.AGRONOMY-BASEL,12(11).
MLA	Mu, Fei,et al."Research on the Classification of Complex Wheat Fields Based on Multi-Scale Feature Fusion".AGRONOMY-BASEL 12.11(2022).